U.S. patent application number 16/977194 was filed with the patent office on 2021-05-27 for method, computing device and wearable device for sleep stage detection.
This patent application is currently assigned to Nitto Denko Corporation. The applicant listed for this patent is Nitto Denko Corporation. Invention is credited to Thada Jirajaras, Maneerat Jiravanichkul, Pannawit Srisukh, Visit Thaveeprungsriporn, Pongsarun Thiamtawan.
Application Number | 20210153807 16/977194 |
Document ID | / |
Family ID | 1000005431844 |
Filed Date | 2021-05-27 |
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United States Patent
Application |
20210153807 |
Kind Code |
A1 |
Jiravanichkul; Maneerat ; et
al. |
May 27, 2021 |
Method, Computing Device And Wearable Device For Sleep Stage
Detection
Abstract
In a described embodiment, a method of sleep stage detection is
disclosed. The method comprises receiving a first vital sign
feature 701a including a plurality of first feature values
v.sub.1-v.sub.11 corresponding to respective ones of a plurality of
epochs; and performing a first logistic regression operation 701a'
to calculate a first indication value 702a for each intermediate
one of the epochs based on the corresponding first feature value
v.sub.6 and those of preceding v.sub.1-v.sub.5 and succeeding
v.sub.7-v.sub.11 ones of the epochs, the first indication value
702a being descriptive of a sleep stage of the corresponding
intermediate epoch. A method of creating a model for logistic
regression, a method of extracting a heart rate variability
feature, a method of creating a model for extracting a heart rate
variability feature, a method of deriving a medical dosage, a
method of assessing a responsiveness to a medical dosage, a
computer-readable medium, a computing device and a wearable device
are also disclosed.
Inventors: |
Jiravanichkul; Maneerat;
(Bangkok, TH) ; Jirajaras; Thada; (Bangkok,
TH) ; Thaveeprungsriporn; Visit; (Singapore, SG)
; Srisukh; Pannawit; (Bangkok, TH) ; Thiamtawan;
Pongsarun; (Bangkok, TH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nitto Denko Corporation |
Osaka |
|
JP |
|
|
Assignee: |
Nitto Denko Corporation
Osaka
JP
|
Family ID: |
1000005431844 |
Appl. No.: |
16/977194 |
Filed: |
February 28, 2019 |
PCT Filed: |
February 28, 2019 |
PCT NO: |
PCT/SG2019/050111 |
371 Date: |
September 1, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 20/10 20180101;
A61B 5/4812 20130101; A61B 5/7221 20130101; A61B 5/7267 20130101;
G16H 40/67 20180101; G16H 50/20 20180101; G16H 50/30 20180101; A61B
5/4848 20130101; A61B 5/02405 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/024 20060101 A61B005/024; G16H 50/30 20060101
G16H050/30; G16H 40/67 20060101 G16H040/67; G16H 20/10 20060101
G16H020/10; G16H 50/20 20060101 G16H050/20 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 2, 2018 |
SG |
10201801850P |
Claims
1. A method of sleep stage detection, comprising: receiving a first
vital sign feature including a plurality of first feature values
corresponding to respective ones of a plurality of epochs; and
performing a first logistic regression operation to calculate a
first indication value for each intermediate one of the epochs
based on the corresponding first feature value and those of
preceding and succeeding ones of the epochs, the first indication
value being descriptive of a sleep stage of the corresponding
intermediate epoch.
2. The method of claim 1, wherein the first vital sign feature
relates to one of a heart rate, a pulse shape variability and a
convoluted high frequency power of heart rate variability.
3. The method of claim 1, wherein the first logistic regression
operation includes weighting and sigmoid operations.
4. The method of claim 1, further comprising: receiving a second
vital sign feature including a plurality of second feature values
corresponding to respective ones of the epochs, the second vital
sign feature belonging to a user of the first vital sign feature;
performing a second logistic regression operation to calculate a
second indication value for each intermediate one of the epochs
based on the corresponding second feature value and those of the
preceding and succeeding ones of the epochs, the second indication
value being descriptive of the sleep stage of the corresponding
intermediate epoch; and performing a further logistic regression
operation to determine the sleep stage of each intermediate one of
the epochs based on the corresponding first and second indication
values.
5. The method of claim 4, wherein one of the logistic regression
operations have weight values different from those of another one
of the logistic regression operations.
6. The method of claim 4, further comprising: receiving a third
vital sign feature including a plurality of third feature values
corresponding to respective ones of the epochs, the third vital
sign feature belonging to the user of the first vital sign feature;
and performing a third logistic regression operation to calculate a
third indication value for each intermediate one of the epochs
based on the corresponding third feature value and those of the
preceding and succeeding ones of the epochs, wherein the further
logistic regression operation is performed to determine the sleep
stage of each intermediate one of the epochs further based on the
corresponding third indication value if the corresponding third
indication value is descriptive of the sleep stage of the
corresponding intermediate epoch.
7. A method of creating a model for logistic regression,
comprising: receiving a vital sign feature in association with
reference sleep stage information; generating, from the vital sign
feature and the reference sleep stage information, a cross
validation set including a plurality of subsamples; calculate each
one of the subsamples with reference to other ones of the
subsamples using a model with each of a plurality of machine
learning parameter sets; and associating the model with one of the
parameter sets based on a comparison of the reference sleep stage
information with a result of the calculation.
8. The method of claim 7, wherein the vital sign feature relates to
one of a heart rate, a pulse shape variability and a convoluted
high frequency power of heart rate variability.
9. The method of claim 7, further comprising: receiving a further
vital sign feature in association with the reference sleep stage
information, the further vital sign feature belonging to a user of
the vital sign feature and being different from the vital sign
feature, wherein the cross validation set is further generated from
the further vital sign feature.
10-13. (canceled)
14. The method of claim 7, wherein generating the cross validation
set comprises: combining the vital sign feature with the reference
sleep stage information to derive the cross validation set.
15-17. (canceled)
18. A method of extracting a heart rate variability feature,
comprising: convolving a high frequency portion of a heart rate
variability power spectral density with a convolution filter to
generate a plurality of convolution values representing respective
patterns of heart rate variability, the convolution filter relating
to a convolutional neural network model; and selecting one of the
convolution values based on a result of an activation function
operation performed on the convolution values.
19. The method of claim 18, wherein the selected convolution value
corresponds to a largest value resulting from the performed
activation function.
20-22. (canceled)
23. A method of creating a model for extracting a heart rate
variability feature, comprising: receiving, in association with
reference sleep stage information, a vital sign feature
representing a heart rate variability power spectral density; and
creating a model including a convolution filter using machine
learning based on the vital sign feature with reference to the
reference sleep stage information.
24-26. (canceled)
27. A method of assessing a responsiveness to a medical dosage,
comprising: receiving sleep stage information of a first time
period, a first dose of the medical dosage occurring before the
first time period; receiving sleep stage information of a second
time period for comparison with that of the first time period, a
second dose of the medical dosage occurring between the first and
second time periods; and assessing a responsiveness to the second
dose of the medical dosage based on a result of the comparison.
28. The method of claim 27, wherein the second dose of the medical
dosage is adjusted from the first dose of the medical dosage.
29. The method of claim 28, wherein the second dose of the medical
dosage is increased from the first dose of the medical dosage.
30. The method of claim 27, wherein a third dose of the medical
dosage is adjustable based on the responsiveness to the second dose
of the medical dosage, the third dose of the medical dosage to
occur between the second time period and a third time period.
31. The method of claim 30, wherein the third dose of the medical
dosage is increased from the second dose of the medical dosage.
32. The method of claim 27, wherein each time period represents at
least one cycle of sleep.
33. The method of claim 27, wherein the medical dosage is a
prescriptive medical dosage that relates to at least one of
anticonvulsant, benzodiazepines and emazepam.
34-40. (canceled)
Description
TECHNICAL FIELD
[0001] The present disclosure relates generally to a method, a
computing device and a wearable device for sleep stage detection,
more particularly for detecting sleep stages including rapid eye
movement (REM) and non-REM (NREM) based on machine learning.
BACKGROUND
[0002] A person's physical and mental states are known to be
influenced by the quality of their sleep. A good quality sleep is
essential for maintaining fitness, wellbeing and good mood. When
asleep, a person typically transitions between a rapid eye movement
(REM) stage and a non-REM (NREM) stage. Several studies have found
that the REM stage plays an important role in mood regulation and
memory consolidation. Furthermore, depressive disorders have been
found to be closely linked to REM sleep dysregulations, such as
increased REM sleep durations and shortened REM latencies. Thus,
the sleep stages may be monitored to provide important information
for sleep behaviour analysis and stress management, as well
depressive disorder treatment, elderly care, and performance
analysis. The quality of analysis and hence the quality of
treatment are largely dictated by the collected data, both in terms
of quality and quantity. To meet these requirements, monitoring
devices employed are typically hardware intensive and complex.
[0003] The current gold standard for sleep study, is
Polysomnography (PSG). This requires numerous sensors, including
Electroencephalography (EEG), Electrooculography (EOG),
Electromyography (EMG), Electrocardiography (ECG), and respiration
sensors. Although PSG provides more information for sleep study, it
is highly immobile and highly-priced, which makes it impractical
for mass observations.
[0004] U.S. Pat. No. 9,655,559B2 discloses automated sleep staging
using wearable sensors. The disclosed arrangement suffers from
several drawbacks, such as poor accuracy and susceptibility to poor
sensor readings.
[0005] It is desirable to provide a method, a computing device and
a wearable device for sleep stage detection, which address at least
one of the drawbacks of the prior art and/or to provide the public
with a useful choice and/or an alternative choice.
SUMMARY
[0006] According to one aspect, there is provided a method of sleep
stage detection, comprising: receiving a first vital sign feature
including a plurality of first feature values corresponding to
respective ones of a plurality of epochs; and performing a first
logistic regression operation to calculate a first indication value
for each intermediate one of the epochs based on the corresponding
first feature value and those of preceding and succeeding ones of
the epochs, the first indication value being descriptive of a sleep
stage of the corresponding intermediate epoch.
[0007] The described embodiment is particularly advantageous. For
example, by performing the first logistic regression operation with
respect to each intermediate one of the epochs based on the first
feature values of the preceding, intermediate and succeeding ones
of the epochs, the first indication value descriptive of the sleep
stage of each intermediate one of the epochs can be calculated.
This is advantageous in that, not only the feature values of the
preceding and intermediate epochs are used, but that of the
succeeding epoch is also used, achieving an improved accuracy. In
one embodiment, due to the minimal amount of data and processing
required, the method requires less computing resources to be
implemented, either in a hardware form, a software form, or a
combination of both. Thus, the method is particularly suitable for
but not limited to implementation using a wearable device.
[0008] The first vital sign feature may relate to one of a heart
rate, a pulse shape variability and a convoluted high frequency
power of heart rate variability. These vital sign features are
found to relate to sleep stage and are therefore useful in
detecting a sleep stage.
[0009] Preferably, the first logistic regression operation may
include weighting and sigmoid operations. Logistic regression
operations are less complex and more interpretable than non-linear
models. It is known in the field of machine learning that complex
models have higher risks of overfitting in comparison with simple
models. Thus, by using logistic regression operations, sleep stage
detection according to one embodiment is suitable to be performed
on, for example, a wearable device. Relatively complex algorithms
used in relation to convolutional neural networks for extracting
HRV PSD, which involves three dimensions, may be performed on, for
example, a server of a cloud service. The extraction of HRV
PSD.
[0010] It is preferred that the method may further comprise:
receiving a second vital sign feature including a plurality of
second feature values corresponding to respective ones of the
epochs; performing a second logistic regression operation to
calculate a second indication value for each intermediate one of
the epochs based on the corresponding second feature value and
those of the preceding and succeeding ones of the epochs, the
second indication value being descriptive of the sleep stage of the
corresponding intermediate epoch; and performing a further logistic
regression operation to determine the sleep stage of each
intermediate one of the epochs based on the corresponding first and
second indication values.
[0011] In the described embodiment, for example, by receiving
multiple vital sign features and performing multiple logistic
regression operations based on the respective vital sign features
to calculate the respective indication values, the further logistic
regression operation can be performed based on the indication
values to determine the sleep stage of each intermediate epoch. By
taking into account information of the multiple vital sign
features, an improved result of sleep stage detection can be
achieved. By taking into account multiple vital sign feature and
performing a further logistic regression operation, the sleep stage
can be detected with an improved accuracy.
[0012] It may be that one of the logistic regression operations
have weight values different from those of another one of the
logistic regression operations.
[0013] Preferably, the method may further comprise: receiving a
third vital sign feature including a plurality of third feature
values corresponding to respective ones of the epochs; and
performing a third logistic regression operation to calculate a
third indication value for each intermediate one of the epochs
based on the corresponding third feature value and those of the
preceding and succeeding ones of the epochs, wherein the further
logistic regression operation is performed to determine the sleep
stage of each intermediate one of the epochs further based on the
corresponding third indication value if the corresponding third
indication value is descriptive of the sleep stage of the
corresponding intermediate epoch.
[0014] This arrangement is advantageous because the third
indication value may not be indicative of the sleep stage due to,
for example, uncompensated corresponding feature values of the
third vital sign feature. Such a third indication value, if used in
the further logistic regression operation, may impact the accuracy
of sleep stage determination. Thus, by only using the third
indication value only if it is descriptive of the sleep stage, an
accurate result of sleep stage detection can be achieved.
[0015] According to another aspect, there is provided a method of
creating a model for logistic regression, comprising: receiving a
vital sign feature in association with reference sleep stage
information; generating, from the vital sign feature and the
reference sleep stage information, a cross validation set including
a plurality of subsamples; calculate each one of the subsamples
with reference to other ones of the subsamples using a model with
each of a plurality of machine learning parameter sets; and
associating the model with one of the parameter sets based on a
comparison of the reference sleep stage information with a result
of the calculation.
[0016] In one embodiment, through the use of machine learning
techniques, correlation between the vital sign features and the
reference sleep stage information can be ascertained efficiently to
determine a parameter set for use with a model. For example, the
parameter set can include a first parameter set retrieved in a
first sleep period, a second parameter set retrieved in a second
sleep period, and both the first and second sleep periods can be
retrieved and/or stored, to be programmed into a separate model.
That is to say, the parameter set for one sleep period can be
different from that for another sleep period. In another example,
for trending the user sleep pattern, a first parameter set is
retrieved in a first sleep period, a second parameter set is
retrieved in a second sleep period, and both the first and second
sleep periods can be retrieved and/or stored, to be programmed in a
trend model. A third parameter set is retrieved in a third sleep
period, where the third sleep period is distinct and separate,
through time space, from the first and second sleep periods. The
third sleep period can be retrieved and/or stored, to be programmed
into the trend model.
[0017] The vital sign feature may relate to one of a heart rate, a
pulse shape variability and a convoluted high frequency power of
heart rate variability. These vital sign features are found to
relate to sleep stage and are therefore useful in detecting a sleep
stage. In another embodiment, the vital sign feature may also
relate to a summation of power in high frequency of heart rate
variability, a summation of power in low frequency of heart rate
variability, LF/HF ratio of the heart rate variability, standard
deviation of heart rate. These vital sign features may be retrieved
from present and/or past data from another device and are therefore
useful in detecting a sleep stage.
[0018] Preferably, the method further comprises: receiving another
vital sign feature in association with the reference sleep stage
information, wherein the cross validation set is further generated
from the another vital sign feature. By taking into account another
vital sign feature, the cross validation set includes more
information that can be used to accurately detect sleep stage.
[0019] The vital sign feature may be sliced by at least three
consecutive epochs. The at least three consecutive epochs may
include, with respect to an intermediate one of the epochs, a
preceding one, the intermediate one and a succeeding one of the
epochs. By taking into account the succeeding epoch, sleep stage
can be detected with an improved accuracy. In a described
embodiment, the at least three consecutive epochs include 11
epochs. In addition, the at least three consecutive epochs are
configured to correspond to a total window period of at least 30
minutes. Sleep data can be more than one hour (e.g. 7-8 hours) in
duration. By "slicing" the sleep data, in one embodiment, into
hourly periods or intervals, the data resemble a sleep cycle, which
allows sleep stage calculation to be performed efficiently.
[0020] The vital sign feature may be normalised. Further, the vital
sign feature may be compensated for a missing portion. For example,
the missing portion can be compensated for using max pooling. Such
a technique is useful for reducing adverse influence of such
missing portions on the detection result, which may otherwise lead
to false positive or negative detection. Alternatively, such
missing portions may be replaced with random values, average
values, nearest values, linear interpolation, etc., depending on
implementation.
[0021] According to another aspect, there is provided a method of
extracting a heart rate variability feature, comprising: convolving
a high frequency portion of a heart rate variability power spectral
density with a convolution filter to generate a plurality of
convolution values representing respective patterns of heart rate
variability, the convolution filter relating to a convolutional
neural network model; and selecting one of the convolution values
based on a result of an activation function operation performed on
the convolution values.
[0022] In one disclosed embodiment, the method is advantageous in
that the correlation between the high frequency portion of the
heart rate variability power spectral density and rapid eye
movement (REM) is taken into consideration, where the detection of
a lower power in the high frequency portion strongly indicates
REM.
[0023] It is preferable that the selected convolution value
corresponds to a largest value resulting from the performed
activation function. The largest value corresponds to REM and/or
NREM sleep stage that advantageously provides a concerted approach
to deriving the sleep stage
[0024] Moreover, the high frequency portion preferably ranges from
0.15 Hz to 0.4 Hz. The heart rate variability power spectral
density is found to have a particularly high degree of relation
with sleep stage in this frequency portion. By taking into account
spectral information in this frequency range, the resultant heart
rate variability feature is more indicative of the sleep stage.
[0025] The power spectral density may be normalised across the high
frequency portion and a low frequency portion thereof. The
normalisation rejects scales of the data but instead provides a
consistent power spectral density for adaptation
[0026] Preferably, the activation function operation relates to one
of a rectified linear unit, a soft relu and a sigmoid function.
[0027] According to another aspect of the present disclosure, there
is disclosed a method of creating a model for extracting a heart
rate variability feature, comprising: receiving, in association
with reference sleep stage information, a vital sign feature
representing a heart rate variability power spectral density; and
creating a model including a convolution filter using machine
learning based on the vital sign feature with reference to the
reference sleep stage information. By deriving the physiological
signal using such a model, the high frequency component of heart
rate variability serves as a relatively consistent basis for sleep
stage detection.
[0028] The vital sign feature is preferably derived from a
physiological signal for an interval of three minutes within each
interval of five minutes. Power consumption can be reduced without
significantly impacting the accuracy of sleep stage detection.
Alternatively, the vital sign feature may be derived from the
physiological signal with respect to an epoch for an interval
shorter than that of the epoch.
[0029] Preferably, the vital sign feature is normalised.
[0030] The machine learning preferably includes a convolutional
neural network. Convolutional neural network, CNN, algorithms for
training models can relate information in both HRV PSD and sleep
stage. HRV PSD can be viewed as an image that can contain a pattern
of HRV changes in frequency domain over a time period. Pattern of
alternating low and high power in HRV PSD that is coherent with REM
and NREM sleep stage patterns can be observed in experimental data.
CNN algorithms can be used to extract information of such an
image.
[0031] In one disclosed embodiment, one advantage of using CNN
algorithms is that CNN algorithms can process HRV PSD into a
calculated value that relates to REM/NREM sleep stage by optimizing
the convolution filter, activation function, data pooling method,
and neural network weights that can provide a minimal or reduced
prediction error. In other words, CNN algorithms can summarize or
otherwise process HRV PSD into a calculated value indicative of a
sleep stage through model training. The present disclosure in one
example arrangement advantageously processes at least three
dimensional data that includes the frequency of HRV, time domain
and Power density.
[0032] It is possible to use information gathered or retrieved to
be eventually applied with other information obtained from another
device together with the information gathered from the wearable
device. Vice versa, retrieve information such as any of the HR,
HRV, and pulse shape variability, from any type of sensors, the
machine learning algorithm can further refine the information.
[0033] According to another aspect, there is disclosed a method of
deriving medical dosage comprising: receiving sleep stage
information of a first time period; receiving and comparing sleep
stage information between the first time period and a second time
period; providing the reference sleep stage information causing for
prescriptive medical dose.
[0034] According to another aspect, there is disclosed a method of
assessing a responsiveness to a medical dosage comprising:
receiving sleep stage information of a first time period; receiving
sleep stage information of a second time period for comparison with
that of the first time period; and assessing a responsiveness to a
medical dosage occurring between the first and second time periods
based on a result of the comparison.
[0035] The prescriptive medical dosage may relate to at least one
of anticonvulsant, benzodiazepines and emazepam.
[0036] According to another aspect of the present disclosure, there
is disclosed a computer-readable medium comprising instructions for
causing a processor to perform any one of the above methods.
[0037] Preferably, the instructions are adapted to be implemented
in firmware. Due to their simplicity, at least some of the above
methods are particularly suitable for, but not limited to,
implementation in the form of instructions in firmware.
[0038] According to another aspect of the present disclosure, there
is disclosed a computing device comprising: a processor; and a
storage device comprising instructions for causing the processor to
perform any of the above methods.
[0039] The storage device is preferably a firmware chip. As alluded
to above, at least some of the above methods may be implemented
using firmware due to their simplicity and low hardware
requirements.
[0040] According to another aspect of the present disclosure, there
is disclosed a wearable device comprising: a storage device
comprising instructions for causing the processor to perform any of
the above methods.
[0041] The storage device is preferably a firmware chip. As
discussed above, at least some of the above methods may be
implemented using firmware.
[0042] It is preferred that the wearable device is in the form of a
wristwatch, which may facilitate long term data collection.
[0043] It is envisaged that features relating to one aspect may be
applicable to the other aspects.
BRIEF DESCRIPTION OF THE DRAWINGS
[0044] Example embodiments will now be described with reference to
the accompanying drawings, among which:
[0045] FIG. 1 illustrates an example system block diagram of a
computing device according to one embodiment of the present
disclosure;
[0046] FIG. 2 shows a flowchart of a method of sleep stage
detection performed by the computing device of FIG. 1;
[0047] FIG. 3A shows a flowchart of a method for creating a model
for logistic regression performed by the computing device of FIG.
1;
[0048] FIG. 3B shows an example of generation of training
dataset;
[0049] FIG. 4 shows a flowchart of a method for extracting a heart
rate variability feature performed by the computing device of FIG.
1;
[0050] FIG. 5 shows a flowchart of a method for creating a model
for extracting a heart rate variability feature performed by the
computing device of FIG. 1;
[0051] FIGS. 6(a) to 6(c) illustrate different scenarios of data
error compensation performed by the computing device of FIG. 1;
[0052] FIG. 7 shows an example scenario of sleep stage calculation
involving a plurality of logistic regression operations followed by
a further logistic regression operation and three vital sign
features, performed by the computing device of FIG. 1;
[0053] FIG. 8 shows an example scenario of the computing device of
FIG. 1 performing a logistic regression operation in a manner of
sliding window;
[0054] FIG. 9 shows line charts of different signals at different
stages of calculation performed by the computing device of FIG.
1;
[0055] FIG. 10 shows a Bland-Altman plot of error percentage for a
result of sleep stage detection obtained using the method of FIG.
2;
[0056] FIG. 11 shows histograms of REM percentage and frequency for
major depressive disorder patients;
[0057] FIG. 12A shows an embodiment of the computing device of FIG.
1 in the form of a wristwatch or wearable device;
[0058] FIG. 12B shows a schematic representation of a head assembly
of the wearable device of FIG. 12A;
[0059] FIG. 12C shows a cross-sectional schematic view of the head
assembly of FIG. 12A; and
[0060] FIG. 13 shows an instance of wireless transmission of
calculated sleep stage information from the wearable device of FIG.
12A to another computing device in an example scenario.
DESCRIPTION
[0061] FIG. 1 shows a system block diagram of an exemplary
computing device 100 suitable for performing embodiments of some
methods of the present disclosure. The computing device 100
includes a processor 101, a firmware chip 102, a storage device
103, a physiological sensor 104 (e.g., an electrocardiogram sensor,
a photoplethysmogram sensor or a combination thereof) and a
communication interface 105 (wired or wireless) operatively
associated with the processor 101. It is worth noting that any type
of sensor capable of physiological measurement can be used. In one
embodiment, the computing device 100 takes on the form of a
miniature device (e.g., a wearable device). In another embodiment,
the computing device 100 takes on the form of a computer (e.g., a
server). In yet another embodiment, the computing device 100 takes
on the form of a mobile device. Instructions executable by the
processor 101 may be stored in the firmware chip 102, the storage
device 103 or a combination thereof.
[0062] FIG. 2 shows a flowchart of a method 200 of sleep stage
detection according to one embodiment of the present disclosure.
Storage and execution of instructions embodying the method 200 are
as described hereinabove. Further, the computing device 100 in this
embodiment takes the form of a wearable sleep stage tracking device
100. In step 201, the processor 101 receives a physiological signal
from the sensor 104 worn on any suitable body part (e.g., the
wrist, the upper arm, the index finger, the neck or the head).
[0063] In step 202, the processor 101 extracts, from the
physiological signal, vital sign features representing a heart rate
(HR), a convoluted high frequency power of heart rate variability
(Conv_HF), and a pulse shape variability (PSV). In other
embodiments, the vital sign features may represent additional
physiological features. The vital sign feature may embody the
exemplary form of a series of feature values each corresponding to
a sleep stage of a corresponding epoch. In other words, each vital
sign feature includes a plurality of feature values corresponding
to a plurality of epochs, respectively. Take the convoluted high
frequency power of heart rate variability for example, each feature
value in the vital sign feature may represent a unique pattern of
variability within the respective epoch. In other embodiments,
however, the processor 101 may extract any number (e.g., 1, 2 or 4)
of vital sign features from physiological signal.
[0064] Pulse Shape Variability (PSV) can be referred to as a
standard deviation of the vital sign feature derived from a pulse
shape of the input physiological signal (e.g., a PPG signal), which
may be calculated based on time interval information, skewness,
magnitude, integral and differential information, a frequency
component, or their derivatives of a pulse for example.
[0065] In step 203, the processor 101 compensate for missing data
portions in the vital sign features, if any. Missing data portions
may arise from, for example, poor sensor signal quality. Through
such compensation, susceptibility of a detection result to missing
data portions can be reduced. Compensation for missing data portion
in the vital sign features is described in detail hereinafter in
relation to FIG. 6. The compensation for missing data portion may
have an extraction of at least two series of inputs. Alternatively,
the compensation for missing data portion may have an extraction of
a set of alternate series of inputs. While another alternative may
have a combination of the extraction of the at least two series of
inputs and the set of alternate series of inputs.
[0066] In step 204, the processor 101 normalise the vital sign
features, whether compensated or not.
[0067] In step 205, the processor 101 calculates (e.g., determines,
detects or classifies) a sleep stage for each epoch from the
processed vital sign features using at least one model (e.g.,
learned model). In this step, the processor 101 performs the
function of a machine learning classifier, calculating a sleep
stage for each epoch from the vital sign features. The sleep stage
is determined to be one of rapid eye movement (REM) and non-REM
(NREM) based a result of the calculation. This step of calculation
is described in further detail hereinafter in relation to FIGS. 7,
8 and 9. The at least one model may be stored on the firmware chip
102 or the storage device 103. However, the at least one model may
be created, developed or trained using machine learning on any
computing platform, which is described in detail hereinafter. Where
the at least one model is separately stored on a cloud server,
retrieval of the at least one model by the processor 101 is, in one
arrangement, based on the processor 101 communicating through the
communication interface 105 connecting or linking to the cloud
server. The vital sign features may, in an alternative embodiment,
be provided by the processor 101 to a cloud server running the at
least one model for sleep stage calculation. Thereafter, the
processor 101 of the computing device 100 may request (e.g.,
retrieve from) the cloud server for the calculated result of sleep
stage. In one embodiment, the calculated result of sleep stage can
be transferred to another device programmed to combine or extract
the calculated result of sleep stage with information in the other
device for calculation. In other words, the calculation of sleep
stage can be partially or wholly offloaded by the processor 101 to
an associated processing platform such as the cloud server. The
calculated or detected sleep stage of each epoch may be processed,
by the processor 101 or otherwise, into a sleep stage
hypnogram.
[0068] For the purpose of step 205, a model is used for machine
learning classification (e.g., logistic regression). Creating
(e.g., training) of the model is described below in relation to
FIG. 3A. Once the new model is trained (i.e., a parameter set is
associated with the model), the associated parameter set, which may
include a set of constant numbers, may be stored on the firmware
chip 102 of the computing device 100. Model training/re-training or
adaptive modelling may be performed in real-time and the parameter
set, if not stored on the firmware chip, may need to be externally
retrieved from, for example an associated server device on which
the parameter set is stored. The parameter set can be transferred
to a server hosting a cloud service so as to enable future,
long-term tracing, trending and tracking of user sleep patterns. In
contrast, existing PSG techniques cannot be used to check user
sleep patterns in such an efficient manner due to their immobile
nature.
[0069] Therefore, it can be understood that the machine learning
algorithms are used for sleep stage detection based on the vital
sign features. The machine learning algorithms may be implemented
using available machine learning tools. The logistic regression may
be implemented using, for example, the MATLAB programming language.
The machine learning algorithm (e.g., logistic regression) searches
for the linear boundary, which is the linear combination of the
input features, that can best separate the input features into two
classes. This can be done by reducing errors of the system in
classifying the training data. The resultant output is a set of
linear combination of the features diverted to being represented by
said one of the parameter sets. This is considered as a model. This
model can be applied with a new set of inputs to detect a class of
interest, which is REM or NREM sleep stage in the scenario of FIG.
2. Machine learning may also be used to derive vital sign features,
which is described below.
[0070] FIG. 3A shows a flowchart of a method 300 of creating a
model for logistic regression, according to one embodiment of the
present disclosure. The computing device 100 in this embodiment
takes the form of a server device 100 configured to perform machine
learning operations. The created model may be used for the purpose
of step 205.
[0071] In step 301, the processor 101 receives, in association with
reference sleep stage information, a physiological signal via the
communication interface 105. The physiological signal may otherwise
be received from the physiological sensor 104 in association with
the reference sleep stage information. The reference sleep stage
information and the physiological signal may be received
simultaneously or otherwise. The reference sleep stage information
is provided by a reference device, which may be one tested and
approved by an authority. The reference sleep stage information can
be derived from, for example, a medical device approved by the FDA
to provide around 89% agreement of sleep stage detection to the
gold standard.
[0072] In step 302, the processor 101 in this embodiment extracts,
from the physiological signal, vital sign features representing a
heart rate (HR), a convoluted high frequency power of heart rate
variability (Conv_HF), and a pulse shape variability (PSV),
respectively. In other embodiments, the vital sign features may
represent additional physiological features of other types.
[0073] In step 303, the processor 101 processes the vital sign
features by, in any suitable order, slicing at least three
consecutive epochs that, in this embodiment, have a total span of
window period of at least one hour, compensating for any missing
data portions and normalising the slices by the standard score of
the slices. Alternatively, the total span of window period may be
at least 30 minutes. In another embodiment, a mixture of different
total spans of window period may be adopted. For example, there may
be at least four window periods of one hour and at least six window
periods of 30 minutes, resulting in a total span of at least seven
hours. For a total span of at least eight hours, the mixture may
be, for example, at least five window periods of one hour and at
least six window periods of 30 minutes.
[0074] In step 304, the processor 101 combines the processed vital
sign features with the reference sleep stage information to
generate a dataset consisting of a plurality of subsamples. A cross
validation set for model training and a blind test set for model
evaluation can be derived from the dataset. The cross validation
set includes, or is partitioned into, (k) equal-sized
subsamples.
[0075] In step 305, the processor 101 calculates (e.g., determines,
predicts or estimates) each one (1) of the subsamples in the cross
validation set with reference to other ones (k-1) of the subsamples
in the cross validation set using a model with each of a plurality
of machine learning parameter sets. In other words, each one (1) of
the subsamples is calculated by the processor 101 using other ones
(k-1) of the subsamples. This calculation process is repeated (k)
times for each machine learning parameter set so that each
subsample in the cross validation set serves as a calculation
target for calculation by the processor 101 using the other
subsamples in the cross validation set. Thus, said one of the
subsample may be referred to as a target subsample unknown to the
model while the other ones of the subsamples may be referred to as
training subsamples serving as a basis for calculating the target
subsample. With such an arrangement, each subsample serves once as
a target subsample without "contaminating" the training subsamples.
The calculated sleep stage information therefore represents a
generalised performance rather than an overfitting performance.
[0076] Following the calculation, the processor 101 combines, for
each parameter set, the calculated target subsamples to form
calculated sleep stage information for comparison with the
reference sleep stage information.
[0077] In step 306, the processor 101 associate the model with one
of the parameter sets based on a comparison of the calculated sleep
stage information with the reference sleep stage information. In
particular, the parameter set which yields the calculated sleep
stage information that most closely resembles the reference sleep
stage information is associated with the model for achieving the
highest calculation accuracy (or the lowest calculation error).
[0078] FIG. 3B shows an example of generation of training dataset,
showing two tables labelled T1 and T2, respectively. In this
example, referring to table T1, a training dataset for training a
logistic regression model of a vital sign feature is created by
extracting the feature values at time t-5 to t-1 (preceding
epochs), t, (intermediate epoch), and t+1 to t+5 (succeeding
epochs), which together represent 11 feature values (X.sub.-5 to
X.sub.t+5) of the vital sign feature. In another example, a
training dataset for training a logistic regression model of a
vital sign feature is created by extracting the feature values at
time t-2 to t-1 (preceding epochs), t, (intermediate epoch), and
t+1 to t+2 (succeeding epochs), which together represent 5 feature
values (X.sub.t-2 to X.sub.t+2) of the vital sign feature. The
corresponding reference sleep stage at the epoch of t is marked by
cell (St). The algorithm then rearranges the 11 feature values
(X.sub.t-5 to X.sub.t+5) and the corresponding reference sleep
stage (St), then the algorithm treats the rearranged feature values
and the corresponding reference sleep stage as a training data set.
The training data set is shown in Table T2. For each intermediate
epoch at the respective time t, the corresponding reference sleep
stage is appended to the corresponding 11 feature values (X.sub.t-5
to X.sub.t+5). A linear relationship between the feature values
(X.sub.t-5 to X.sub.t+5) at the 11 consecutive epochs and
indicative of the corresponding reference sleep stage (St) of REM
or NREM can be calculated. The linear relationship defines weights
and can be used to calculate an indication value at a particular
epoch corresponding to one of REM and NREM with low error. In
another embodiment, the time taken to extract the feature values of
each vital sign feature may be extended to be longer.
[0079] FIG. 4 shows a flowchart of a method 400 of extracting a
heart rate variability feature, which can be performed
independently or as part of step 202 of the method 200. The
computing device 100 in this embodiment takes the form of a
wearable sleep stage tracking device 100.
[0080] In step 401, the processor 101 extracts, from a
physiological signal received by the computing device 100, a heart
rate variability power spectral density based on pulse interval
detection.
[0081] In step 402, the processor 101 normalises the power spectral
density by dividing power at each frequency by a total power across
low and high frequency portions of the spectrum, where the low
frequency portion ranges from 0.04 Hz to 0.15 Hz and the high
frequency portion ranges from 0.15 Hz to 0.4 Hz.
[0082] In step 403, the processor 101 convolves the high frequency
portion of the power spectral density with a convolution filter to
generate a plurality of convolution values representing respective
heart rate variability features. Each convolution value represents
a pattern of high and low powers in the high frequency portion of
the heart rate variability power spectral density. The convolution
filter relates to a convolutional neural network model, which will
be described hereinafter.
[0083] In step 404, the processor 101 performs an activation
function on the convolution values and selects one of the
convolution values based on a result of the activation function. In
particular, the result of the activation function indicates a
resemblance relationship between the pattern of each convolution
value with the actual sleep stage. The selected convolution value
serves as a vital sign feature representing a convoluted high
frequency power of heart rate variability (Conv_HF).
[0084] FIG. 5 shows a flowchart of a method 500 of creating a model
for extracting a heart rate variability feature, according to one
embodiment of the present disclosure. The computing device 100 in
this embodiment takes the form of a desktop personal computer 100
with sufficient processing resources for machine learning
operations.
[0085] In step 501, the processor 101 receives, in association with
reference sleep stage information, a physiological signal via the
communication interface 105. The physiological signal may otherwise
be received from the physiological sensor 104 in association with
the reference sleep stage information. The reference sleep stage
information and the physiological signal may be received
simultaneously or otherwise. The reference sleep stage information
is provided by a reference device, which may be one tested and
approved by authority. Alternatively, the reference sleep stage
information may be provided by a wearable device tested and
approved by an authority.
[0086] In step 502, the processor 101 extracts, from each interval
of the physiological signal of at least three minutes (e.g., five
minutes), a vital sign feature representing a heart rate
variability power spectrum density. The reason for dividing the
signal into intervals of at least three minutes is that this step
is for dividing a signal into several epochs and for extracting
features in each epoch. In other words, extraction of the vital
sign feature from the physiological signal is performed for each of
a plurality of intervals into which the physiological signal is
divided, with each interval being at least three minutes in
duration. Without performing this step, the extracted HRV feature
will represent the signal of, for example, the whole sleep session
instead of specific time periods or epochs during the session. In
this embodiment, each interval is five minutes and, within each
interval, only three minutes of the corresponding portion of the
physiological signal are used for deriving or extracting the vital
sign features. In other words, the vital sign feature is derived
from the physiological signal for three minutes within each
interval of five minutes. This means that within each interval of
five minutes, the relevant components for feature extraction
operate only for three minutes, thereby reducing power consumption.
In other embodiments, each interval may be other than five minutes,
and within the interval, the corresponding portion of the
physiological signal used for feature extraction may be other than
three minutes in duration.
[0087] In step 503, the processor 101 normalises the power spectral
density by dividing power at each frequency by a total power across
low and high frequency portions of the spectrum, where the low
frequency portion ranges from 0.04 Hz to 0.15 Hz and where the high
frequency portion ranges from 0.15 Hz to 0.4 Hz.
[0088] In step 504, the normalized HRV PSD and reference's sleep
stages of the training data set are inputted into machine learning
algorithm, particularly convolutional neural network. The model,
which comprises convolution filter, activation function, and data
pooling, with acceptable or highest accuracy is selected and used
for extracting Conv_HF feature, for example, in the method 400.
[0089] In particular, the model can be trained with the
convolutional neural network, CNN, and algorithm using existing
toolboxes, such as "mxnet" based on the programming language "R".
Firstly, a training set comprising HRV PSD (feature) and the
associated REM/NREM sleep stages (label) are input into the CNN
algorithm. The CNN algorithm performs training by calculating the
best convolution filter and neural network weights that yield the
lowest classification or detection error. The training process
begins with a random initialization of the model filter and
weights. Next, backpropagation process is performed to calculate a
gradient toward to a lower error and to adjust weights in every
iteration. To obtain the model that gives a reduced or minimal
error without overfitting, the model is fine-tuned by determining
the appropriate model parameters (e.g. size of convolution filter,
type of activation function, data pooling method, number of hidden
layers, training iteration) that give the lowest error in the
validation set. In such a manner, the optimized model that
comprises convolution filter, activation function, and data pooling
method is selected and is used to derive HRV PSD into Conv_HF
feature.
[0090] As discussed hereinabove in relation to steps 203 and 303,
data error (e.g., missing data portions) may occur. Where the
processor 101 detects such an error in a vital sign feature, the
processor 101 in this embodiment attempts to compensate for the
data error. If the data error is thus rectified, the compensated
vital sign feature is used in the subsequent steps for sleep stage
detection. However, if the data error persists, the epoch is
identified to be "unknown" in order to preserve the reliability of
sleep stage detection. FIGS. 6(a) to 6(c) illustrate respective
scenarios of data error compensation by the processor 101 with
respect to a vital sign feature. The vital sign feature is
exemplified to take the form of a sequence of feature values each
corresponding to a respective epoch.
[0091] In FIG. 6(a), the processor 101 detects no missing feature
value in the vital sign feature 601. No compensation is performed
and the feature values of the vital sign feature are used in the
subsequent step (e.g., algorithm operations) 602. In FIG. 6(b), the
processor 101 detects three missing feature values of zero in the
vital sign feature 603, two of which are consecutive. The processor
101 compensate for the missing feature values by, for each
intermediate feature value with a preceding feature value and a
succeeding feature value, selecting the largest one of the three
feature values to form a new series of feature values of a
compensated vital sign feature 605. Such a technique may be
referred to as "max pooling" 604. The compensated vital sign
feature 605 is in turn used in the subsequent step 606 (e.g.,
algorithm operations). In FIG. 6(c), the processor 101 detects four
missing feature values of zero, three of which are consecutive, in
the vital sign feature 607. The processor 101 compensate for the
missing feature values in a similar manner using max pooling 608,
resulting in a compensated vital sign feature 609. In this
scenario, the three consecutive missing feature values, after
compensation, result in another feature value of zero in the
compensated vital sign feature 609. Since the data error persists,
the processor 101 returns an unknown sleep stage (represented by a
feature value of "0" in this case) for the epoch with the
persistent error (the shaded block) in step 610 to preserve the
reliability of sleep stage detection. It should be noted that other
forms of data error correction or compensation are applicable.
[0092] Step 205 of the method 200 as discussed above involves
calculating a sleep stage for each epoch using a model. Shown in
FIG. 7 is an example scenario where the processor 101 calculates in
step 205 a sleep stage for each intermediate epoch from three vital
sign features 701a-701c (e.g., a heart rate, a pulse shape
variability and a convoluted high frequency power of heart rate
variability, respectively), with each vital sign feature 701a-701c
representable by a respective series of feature values
v.sub.1-v.sub.11 (only 11 shown for each vital sign feature
701a-701c in FIG. 7). For each vital sign feature 701a-701c, the
processor 101 performs a respective logistic regression operation
701a'-701c' to calculate an indication value 702a-702c for each
intermediate epoch (i.e., each intermediate one of the epochs)
based on the corresponding feature value v.sub.6 and those
v.sub.1-v.sub.6, v.sub.7-v.sub.11 of neighbouring epochs. The
neighbouring epochs are epochs temporally neighbouring the
respective intermediate epoch. In this case, the neighbouring
epochs includes five preceding epochs (corresponding to the
respective feature values v.sub.1-v.sub.5), which are five epochs
immediately preceding the intermediate epoch (corresponding to the
feature value v.sub.6), and five succeeding epochs (corresponding
to the respective feature values v.sub.7-v.sub.11), which are five
epochs immediately succeeding the intermediate epoch. However, in
other embodiments, the neighbouring epochs may include any numbers
(e.g., 1, 2 or 3) of preceding and succeeding epochs. Each
indication value 702a-702c is descriptive or indicative of the
sleep stage of the respective intermediate epoch.
[0093] In this embodiment, each logistic regression operation
701a'-701c' is performed by the processor 101 using (or with
reference to) a respective model representing weight and sigmoid
functions. That is to say, for each of the vital sign features
701a-701c, the processor 101 performs the respective logistic
regression operation 701a'-701c' to calculate the indication value
702a-702c for each intermediate one of the epochs based on the
corresponding feature value v.sub.6 and those v.sub.1-v.sub.5,
v.sub.7-v.sub.11 of the neighbouring ones of the epochs. The
indication value 702a-702c thus calculated is descriptive or
indicative of the sleep stage of the corresponding intermediate
epoch. More particularly, for each model, the model includes a
plurality of weights and a sigmoid function. The number of the
weights corresponds to the total number of the preceding,
intermediate and succeeding epochs, which, in this embodiment, is
11. In this embodiment, the models use the same sigmoid functions
with respective sets of weights. The set of weights for one model
may be different from those for another model. The terms "weight"
and "weights" as used herein means "weight value" and "weight
values", respectively. As alluded to above, the weights of each
vital sign feature represent a relationship (a linear combination)
of the feature values of the vital sign feature that best or
ideally represent or describe the sleep stage of a particular
epoch. Thus, each such linear combination of one vital sign feature
may be same or different from that of another vital sign feature
(HR, Conv_HF, PSV, etc. . . . ).
[0094] In the same example of FIG. 7, after calculating the
indication values 702a-702c for the intermediate epoch, the
processor 101 performs a further logistic regression operation 702'
to determine the sleep stage of the intermediate epoch based on the
corresponding indication values 702a-702c. The further logistic
regression operation 702' uses a model independent of or separate
from those of the logistic regression operations 701a'-701c'.
Specifically, the processor 101 performs on the indication values
702a-702c of the respective intermediate epoch the further logistic
regression operation to calculate an REM value 703 for the
respective intermediate epoch. For the respective intermediate
epoch, the processor 101 determines that the epoch corresponds to
REM if the REM value exceeds a predetermined threshold (e.g., 0.5)
and to NREM if otherwise. In the event of missing data as discussed
in relation to FIG. 6, an epoch is determined to correspond to an
unknown sleep stage. Each logistic regression operation 701a'-701c'
(marked in FIG. 7 by the indication "Regression") may be considered
to be a first-stage logistic operation whilst the further logistic
operation 702' (marked in FIG. 7 by the indication "Further
regression") may be considered to be a second-stage logistic
operation.
[0095] It can be appreciated that each logistic regression
operation 701a'-701c' relies on feature values of the respective
vital sign feature extracted from the input physiological signal
(e.g., a PPG signal) to calculate the respective indication value
702a-702c for each epoch. The further logistic regression operation
702' then takes the resultant indication values 702a-702c of each
epoch as inputs to calculate an REM value 703 for the epoch. In
this embodiment, the further logistic regression operation 702'
uses the same sigmoid operation as the operations 701a'-701c' but
with different weights.
[0096] It is worth noting that the further logistic regression
operation 702' can be performed with at least two of the
indications values 702a-702c. That is to say, where one of the
indication values 702a-702c of a particular epoch is
non-descriptive or inconclusive (e.g., having a value of zero) due
to, for example, unsuccessful compensation or non-compensation, the
further logistic regression operation 702' is performed based on
the other two of the indication values 702a-702c of the particular
epoch. That is to say, the further logistic regression operation
702' is performed further based on said one of the indication
values 702a-702c if said one of the indication values 702a-702c is
descriptive of the sleep stage of the particular epoch. In other
words, the further logistic regression operation 702' requires at
least two indication values that are descriptive of the
corresponding epoch, allowing any additional, non-descriptive
indication values to be omitted from the further logistic
regression operation 702'. This prevents or reduces accuracy of
sleep stage detection from being adversely affected by indication
values that are non-descriptive of the sleep stage, which may be
caused by unsuccessful sensor detection. To facilitate this, the
logistic regression operations 701a'-701c' may use models
independent of or separate from one another.
[0097] The process of FIG. 7 is performed, in this embodiment,
sequentially for each intermediate epoch in the manner of a sliding
window (11 epochs) of logistic regression. With reference to FIG.
8, the processor 101 performs the logistic regression operation
701a'-701c' with respect to each vital sign feature 701a-701c to
calculate an indication value 702a-702c for each intermediate epoch
of the vital sign feature 701a-701c. Normalised feature values of
one of the vital sign features 701a-701c are shown in a line chart
801. Among all epochs represented by a series of blocks 802, the
intermediate epochs for sleep stage calculation are represented by
respective ones of unshaded blocks 802b and diagonally shaded
blocks 802c. The normalised feature values of the line chart 801
range from -1 to 2 in this embodiment, and may have a different
range in other embodiments. The first five epochs (considered
non-intermediate) represented by shaded blocks 802a are each
predetermined to have a feature value of zero and are identified as
NREM. This is consistent with the fact that a sleep session begins
with a NREM period, which typically ranges from 30 minutes to 70
minutes. In the sliding window manner, the processor 101
sequentially calculates an indication value 702a-702c for each
subsequent, intermediate epoch towards the end of the sleep session
(marked by the last one of the diagonally shaded blocks 802c), in
the manner described above. The calculated indication values
702a-702c are shown in a line chart 803.
[0098] To perform the calculation for the last five epochs (marked
by the diagonally shaded blocks 802c) which have fewer than five
succeeding epochs, the vital sign feature 701a-701c is extended by
five extension epochs (not shown) with feature values mirroring
those of the last five epochs. The dashed line portion (marked by
"AA" in FIG. 8) at the end of the line chart 801 corresponds to the
extension epochs and mirrors the solid line portion (marked by "BB"
in FIG. 8) corresponding to the last five epochs preceding the
extension epochs. The dashed line portion ("AA") may otherwise be
considered to be a flipped or mirrored extension of the solid line
portion ("BB") of the last five epochs concatenated to the same.
Thus, the last five epochs at the end of the sleep session (i.e.,
the diagonally shaded blocks 802c) also serve as intermediate
epochs for the purpose of the calculation. In the manner described
hereinabove, the indication values 702a-702c are calculated for
respective intermediate epochs of the vital sign feature 701a-701c.
In other embodiments, the calculation of the indication value
702a-702c for each intermediate epoch may take into account any
equal or unequal numbers of preceding and succeeding epochs. For
example, the numbers of preceding and succeeding epochs may be two,
whereby, for the calculation of the indication value of each
intermediate epoch, five epochs are used.
[0099] FIG. 9 is a diagram showing line charts 901 of three vital
sign features (HR, Conv_HF and PSV), line charts 902 of indication
values calculated from the vital sign features using the logistic
regression operations, a line chart 903 of REM values calculated
from the indication values using the further logistic regression
operation, a line chart 904 of sleep stage information derived from
the line chart 903 based on a threshold value, and a line chart 905
of reference sleep information for comparison with the line chart
904. For each epoch, the epoch is determined to correspond to REM
if the corresponding REM value exceeds the threshold value (e.g.,
0.5), and to correspond to NREM if otherwise. The threshold value
may be other than 0.5, depending on implementation.
[0100] FIG. 10 is a Bland-Altman plot showing an error percentage
of REM values calculated according to the embodiment of FIG. 1 with
reference to reference sleep stage information over 50 nights. The
x-axis shows mean values of error percentage while the y-axis shows
values representing differences of error percentage. The horizontal
line shows a mean difference value of 0.09%, while the dashed lines
above and below the horizontal line show confident intervals of
error of -12.10 and 12.28, respectively. The closer the horizontal
line to zero and the closer the dots are from the horizontal line,
the more accurate the calculated REM values.
[0101] FIG. 11 shows first and second rows 1110, 1120, each of
which includes three histograms of REM percentage determined in the
manner described above for a respective test subject. The test
subject in this example is a depressive disorder patient, and in
other applications may be a healthy person wishing to make
adjustments to his or her sleep pattern. Each row 1110, 1120
includes a first histogram 1111, 1121 corresponding to a first
period (labelled as "Period 1"), a second histogram 1112, 1122
corresponding to a second period (labelled as "Period 2") after the
first period, and a third histogram 1113, 1123 corresponding to a
third period (labelled as "Period 3") after the second period. The
test subject of the first row 1110 receives the same, unadjusted
dosage of anticonvulsant before the first period, between the first
and second periods, and between the second and third periods. The
test subject of the second row 1120 receives an initial dosage of
anticonvulsant before the first period, an increased dosage of
anticonvulsant between the first and second periods, and a further
increased dosage of anticonvulsant between the second and third
periods. It can be observed that the patient of the second row 1120
continues to experience high frequency of occurrence of sleep with
high REM percentage range. Each of the first, second and third
periods represents a full cycle of sleep (i.e., a full sleep
session) in this scenario. In other applications, each period may
represent more cycles of sleep. Different test subjects may have
full cycles of sleep of different lengths.
[0102] The information represented by the histograms 1111-1113,
1121-1123 may serve as a basis for determining a prescriptive
medical dose or dosage to be administered to the test subjects for
adjusting REM range and frequency experienced by the test subjects.
The prescriptive medical dose or dosage may relate to
anticonvulsant, benzodiazepines, emazepam, or any receptor agonists
capable of alleviating insomnia.
[0103] Specifically, a method of assessing a responsiveness to a
medical dosage according to one embodiment includes: receiving
sleep stage information of a first time period; receiving sleep
stage information of a second time period for comparison with that
of the first time period; and assessing a responsiveness to a
medical dosage occurring between the first and second time periods
based on a result of the comparison. In the example of the test
subject of the first row 1110, between the second and third
periods, an assessment of responsiveness of the test subject to the
unadjusted medical dosage occurring between the first and second
periods can be made by receiving and comparing the sleep stage
information of the first and second periods. In the example of the
test subject of the second row 1120, between the second and third
periods, an assessment of responsiveness of the test subject to the
increased medical dosage occurring between the first and second
periods can be made by receiving and comparing the sleep stage
information of the first and second periods. A result of the
assessment may serve as basis for maintaining or adjusting a
further medical dosage to occur between the second and third
periods, with the responsiveness of the test subject to the further
medical dosage to be assessed after the third period. It can be
then appreciated that a method of deriving a medical dosage
according to one embodiment includes: receiving sleep stage
information of a first time period; receiving sleep stage
information of a second time period for comparison with that of the
first time period; and providing a medical dosage based on a result
of the comparison.
[0104] FIG. 12A shows an embodiment of the computing device 100 of
FIG. 1 in the form of a wristwatch or wearable device 1200. Further
referring to FIGS. 12B and 12C, the wearable device 1200 includes a
head assembly 1210, which includes, among other components, two
physiological sensors 1211 (one of which is the physiological
sensor 104 of FIG. 1), an illumination element 1212 and a wireless
communication module 1213. The physiological sensors 1211 and the
illumination element 1212 together form the physiological sensor
104 operatively associate with the processor 101. The wireless
communication module 1213 serves to provide the wireless function
of the communication interface 105 associated with the processor
101. The firmware chip 102 and/or the storage device 103 may store
the instructions for the processor 101 to perform any of the steps
of the methods of 200-500. For example, for the purpose of step
201, the physiological sensors 1211 and the illumination element
1212 are controlled by the processor 101 to capture physiological
signals of a user wearing the wearable device 1200. Further, where
the wearable device 1200 performs step 205, the calculated sleep
stage of each epoch may be wirelessly provided by the processor 101
via the wireless communication module 1213. The calculated sleep
stage thus transmitted may constitute sleep stage information.
Further referring to FIG. 13, another computing device 1300 (e.g.,
a personal computer or a server device with components similar to
those shown in FIG. 1) may wirelessly receive, from the wearable
device 1200, such sleep stage information of first and second time
periods and compare the received sleep stage information. The
computing device 1300 may then provide a medical dosage based on a
result of the comparison, or may assess a responsiveness to a
medical dosage occurring between the first and second time periods
based on a result of the comparison.
[0105] In an alternative embodiment involving a single vital sign
feature (e.g., the first vital sign feature 701a), since the first
indication value 702a of an intermediate epoch resulting from the
corresponding first logistic regression operation 701a' is
descriptive of the sleep stage of the intermediate epoch, the
further logistic regression operation 702' can be omitted and the
first indication value 702a may serve as the REM value 703 for
sleep stage determination.
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